calculateContinuousEntropy_Quantile: calculate differential entropy of a continuous value (X)...

In terminological/tidy-info-stats: Functions for manipulating information statistics

calculate differential entropy of a continuous value (X) using a quantile function smoothing approach: Entropy of continuous data - e.g. SGolay approach / discretisation

Description

The Savitsky Golay filter width required to make this at all accurate needs a reasonable amount of data ~ 20 points S. M. Sunoj and P. G. Sankaran, “Quantile based entropy function,” Stat. Probab. Lett., vol. 82, no. 6, pp. 1049–1053, Jun. 2012, doi: 10.1016/j.spl.2012.02.005. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0167715212000521

Usage

calculateContinuousEntropy_Quantile(df, continuousVar, k_05 = 10, ...)

Arguments

df	- may be grouped, in which case the grouping is interpreted as different types of discrete variable
continuousVar	- the column of the continuous value (Y)
k_05	- the half window width of the SG filter that smooths the data. This is dependent on data but typically not less that 10.
groupVars	- the columns of the discrete value quoted by the vars() function (e.g. ggplot facet_wrap)
collect	- if TRUE will collect dbplyr tables before processing, otherwise (the default) will fail on dbplyr tables

Value

a dataframe containing the disctinct values of the groups of df, and for each group an entropy value (H). If df was not grouped this will be a single entry

terminological/tidy-info-stats documentation built on Nov. 19, 2022, 11:23 p.m.